eugeniopaglino / cis519_finalproject

School dropout is a major problem in developing countries and it impairs their ability to create human capital. Using machine learning models schools could identify and target the students at risk of dropping out and enroll them in a support program while keeping the total cost as low as possible. Using data from the India Human Development Survey, we show that it is possible to reach 70% of the students at risk by enrolling in the program ~23% of the kids. If a model similar to the ones we propose would be widely adopted it would increase the cost-effectiveness of programs targeted at keeping kids in education allowing both to reduce the prevalence of dropout and use the available resources efficiently.

• 👋 Hi, I’m @eugeniopaglino. I am a doctoral student in demography and sociology and an MA candidate in statistics and data science at the University of Pennsylvania
• 👀 I’m interested in Bayesian statistics, computational social sciences, data visualisations, and GIS. I love maps!
• 💞️ I’m looking to collaborate on computational social science projects, visualisation projects, or machine learning applications
• 📫 My email is [email protected]